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Executive Quantitative Analyst Jobs (NOW HIRING)

Wsfsbank

Quantitative Risk Analyst

Philadelphia, PA · On-site

$64K - $105K/yr

Maintain current/develop new analytical reports and presentations for senior management, executive ... Strong quantitative and analytical skills in statistical analysis and data science best practices

WSFS Bank

Quantitative Risk Analyst

Philadelphia, PA

$64K - $105K/yr

Maintain current/develop new analytical reports and presentations for senior management, executive ... Strong quantitative and analytical skills in statistical analysis and data science best practices

WSFS Bank

Quantitative Risk Analyst

Philadelphia, PA · On-site

$64K - $105K/yr

Maintain current/develop new analytical reports and presentations for senior management, executive ... Strong quantitative and analytical skills in statistical analysis and data science best practices

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Executive Quantitative Analyst information

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$56.5K

$133.9K

$240K

How much do executive quantitative analyst jobs pay per year?

As of Aug 11, 2026, the average yearly pay for executive quantitative analyst in the United States is $133,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $145,500.00 per year, depending on experience, location, and employer.

What is the difference between Executive Quantitative Analyst vs Quantitative Analyst?

AspectExecutive Quantitative AnalystQuantitative Analyst
CredentialsAdvanced degrees (Master's/PhD), certifications like CFA or CQFBachelor's or Master's in finance, mathematics, or related fields
Work EnvironmentStrategic roles, senior teams, decision-making focusData analysis, model development, and implementation
Employer & IndustryFinancial institutions, hedge funds, asset management firmsSame industries, often entry to mid-level roles

The main difference is that Executive Quantitative Analysts hold senior, strategic roles with leadership responsibilities, while Quantitative Analysts focus on data analysis and model development. Executive roles require more experience and advanced credentials, whereas Quantitative Analysts are often earlier in their careers.

What is an executive quantitative analyst?

Executive Quantitative Analysts are senior professionals who apply advanced mathematical, statistical, and computational techniques to analyze data and support high-level decision-making within an organization. They often oversee teams of analysts, develop complex financial or business models, and advise executives on strategies based on quantitative insights. Their work is critical in industries such as finance, consulting, and technology, where data-driven decisions can have significant impacts on business outcomes.

What are the key skills and qualifications needed to thrive as an executive quantitative analyst, and why are they important?

To thrive as an Executive Quantitative Analyst, you need advanced quantitative analysis skills, a strong background in mathematics or statistics, and typically a master's or PhD in a quantitative field. Expertise in programming languages like Python or R, experience with data visualization tools, and knowledge of financial modeling platforms are commonly required. Exceptional problem-solving abilities, strategic thinking, and clear communication set top performers apart in this role. These skills are crucial for generating actionable insights, influencing high-level decisions, and driving organizational success through data-driven strategies.

What are some typical challenges an executive quantitative analyst faces when leading data-driven initiatives within financial institutions?

Executive Quantitative Analysts often encounter challenges in aligning complex analytical models with business objectives, especially when communicating technical findings to non-technical stakeholders. Balancing the need for model accuracy with regulatory compliance and risk management can also be demanding. Additionally, collaborating across departments such as IT, risk, and trading requires strong leadership and the ability to translate quantitative insights into actionable strategies that drive organizational success.
What cities are hiring for Executive Quantitative Analyst jobs? Cities with the most Executive Quantitative Analyst job openings:
What are the most commonly searched types of Quantitative Analyst jobs? The most popular types of Quantitative Analyst jobs are:
What states have the most Executive Quantitative Analyst jobs? States with the most job openings for Executive Quantitative Analyst jobs include:
Executive Quantitative Analyst jobs near you
Infographic showing various Executive Quantitative Analyst job openings in the United States as of July 2026, with employment types broken down into 89% Full Time, 6% Part Time, 1% Temporary, and 4% Contract. Highlights an 83% Physical, 7% Hybrid, and 10% Remote job distribution, with an average salary of $133,877 per year, or $64.4 per hour.

Quantitative Analyst

Wright-Patt Credit Union

Beavercreek, OH • On-site

Full-time

Posted 19 days ago

Wright-Patt Credit Union rating

5.8

Company rating: 5.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description

The Quantitative Analyst is responsible for leading high-impact statistical analysis, measurement design, and scalable analytics solutions that improve business performance and decision-making. This role partners closely with Strategy, Product, and Technology teams to evaluate key initiatives, identify performance drivers, develop statistically sound measurement approaches, and deliver executive-ready insights that influence priorities and investments. The Quantitative Analyst combines strong analytical depth with automation and repeatability, ensuring insights are accurate, timely, and operationally useful.

1)      High-Impact Quantitative Analysis & Decision Science (30%): Use statistical methods to identify drivers of performance, validate hypotheses, and quantify the impact of business decision using structured and repeatable approaches.

a)       Perform exploratory data analysis, segmentation, and trend analysis to uncover patterns and anomalies.

b)      Apply statistical techniques such as hypothesis testing, confidence intervals, correlation, and regression analysis.

c)       Identify opportunities for growth, efficiency, and experience improvement using data-backed recommendations.

d)      Deliver decision-ready outputs that connect analysis to actions, tradeoffs, and expected outcomes.

2)      Experimentation, Testing, and Impact Evaluation (25%): Design measurement frameworks that ensure the organization can track initiative performance, quantify impact, and drive accountability.

a)       Support A/B testing and experiment analysis including test design inputs, lift measurement, and interpretation.

b)      Partner with product and business teams to define success metrics, baselines, and measurement plans.

c)       Evaluate initiative effectiveness using controlled comparisons, pre/post analysis, and statistical significance testing.

d)      Develop standardized experiment readouts and decision frameworks to improve speed and consistency.

3)      Predictive Analytics & Optimization (20%): Drive advanced analytics efforts that improve targeting, prioritization, and decision-making through modeling and quantitative scoring.

a)       Partner with data scientists to support model development by preparing datasets, validating features, and interpreting outputs.

b)      Build and maintain scoring frameworks (propensity, prioritization, classification support) aligned to business use cases.

c)       Support model evaluation using practical performance measures (lift, precision/recall, error rates).

d)      Translate model outputs into actionable recommendations and operational workflows.

4)      Automation & Scalable Analytics Delivery (15%): Increase speed, consistency, and reliability of insights by automating analysis workflows and enabling scalable analytics delivery.

a)       Develop automated analysis workflows using SQL and Python to reduce manual effort.

b)      Build reusable scripts, templates, and standardized datasets to improve reliability and consistency.

c)       Partner with data engineering teams to improve data availability and support repeatable pipelines.

d)      Implement monitoring and alerting for key performance indicators and threshold-based changes.

5)      Communication, Visualization, and Executive Enablement (10%): Present actionable insights to senior leadership in a format that is relevant for the audience.

a)       Build clear, executive-ready summaries and visualizations tied to business outcomes.

b)      Present findings and recommendations to senior leaders and cross-functional teams.

c)       Communicate confidence levels, limitations, and tradeoffs in a practical way.

d)      Ensures proper policies, procedures, risk mitigation activities, and operating controls are followed. Reports gaps in policies, procedures, and operating controls to leadership to ensure member impact and risk is mitigated.  

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